Quantum Computing Control Center Platform
The most advanced quantum computing platform featuring real-time telemetry, Bayesian optimization algorithms, and enterprise-grade monitoring—engineered for breakthrough quantum research and development.
The most advanced quantum computing platform featuring real-time telemetry, Bayesian optimization algorithms, and enterprise-grade monitoring—engineered for breakthrough quantum research and development.
Current ion-trap quantum systems achieve impressive 99.9% single-rack fidelity, but face a fundamental barrier: photonic links between racks degrade exponentially with distance and environmental noise.
Real-time monitoring and auto-tuning that adapts to photonic channel degradation in microseconds, maintaining coherence across distributed quantum networks.
Dynamic error correction algorithms that adjust in real-time to maintain 99%+ fidelity across photonic links.
Synchronizes quantum states across multiple racks using predictive noise modeling and pre-emptive compensation.
A multi-layered system designed for real-time quantum control with microsecond precision and enterprise-grade reliability.
Ultra-low latency data ingestion layer handling quantum state measurements and environmental sensor data with sub-millisecond processing guarantees.
use tokio::net::TcpListener;
use tokio_util::codec::{FramedRead, LengthDelimitedCodec};
// Lock-free ring buffer for telemetry streams
struct TelemetryBuffer {
buffer: Arc<RingBuffer<QuantumEvent>>,
producer: Producer<QuantumEvent>,
consumer: Consumer<QuantumEvent>,
}
async fn handle_quantum_stream(stream: TcpStream) {
let mut framed = FramedRead::new(stream, LengthDelimitedCodec::new());
while let Some(frame) = framed.try_next().await? {
// Zero-copy event processing
let event = unsafe { deserialize_unchecked(&frame) };
telemetry_buffer.push_nonblocking(event).await;
}
}
Real-time quantum fidelity estimation using hierarchical Bayesian models that adapt to hardware drift and environmental changes.
import pymc as pm
import numpy as np
from scipy import stats
class QuantumFidelityTracker:
def __init__(self, gate_count=64):
with pm.Model() as self.model:
# Hierarchical priors for gate fidelities
alpha_pop = pm.Gamma("alpha_pop", alpha=2, beta=0.1)
beta_pop = pm.Gamma("beta_pop", alpha=2, beta=0.1)
# Individual gate fidelities
self.alpha = pm.Gamma("alpha", alpha=alpha_pop,
beta=1, shape=gate_count)
self.beta = pm.Gamma("beta", alpha=beta_pop,
beta=1, shape=gate_count)
# Observed success rates
self.theta = pm.Beta("theta", alpha=self.alpha,
beta=self.beta, shape=gate_count)
def update_posterior(self, successes, trials):
"""Real-time Bayesian update with streaming data"""
with self.model:
obs = pm.Binomial("obs", n=trials, p=self.theta,
observed=successes)
# Incremental NUTS sampling
trace = pm.sample(1000, tune=500, chains=2,
return_inferencedata=True)
return trace.posterior.theta.mean(dim=["chain", "draw"])
Graph-theoretic circuit optimization using ZX-calculus rewrite rules and Clifford+T synthesis for significant depth reduction.
import pyzx as zx
from pyzx.circuit import Circuit
from pyzx.optimize import clifford_simp, full_reduce
class QuantumCircuitOptimizer:
def __init__(self):
self.optimization_passes = [
zx.spider_simp, # Spider fusion
zx.id_simp, # Identity removal
zx.pivot_simp, # Pivot simplification
zx.lcomp_simp, # Local complementation
]
def optimize_circuit(self, qasm_circuit):
"""Apply CliNR passes for circuit optimization"""
# Convert QASM to ZX-graph
circuit = Circuit.from_qasm(qasm_circuit)
graph = circuit.to_graph()
# Apply optimization passes
original_gates = len(circuit.gates)
for pass_func in self.optimization_passes:
zx.simplify.simp(graph, pass_func)
# Clifford+T synthesis with T-count minimization
circuit_opt = zx.extract_circuit(graph.copy())
# Verify identity preservation
assert zx.compare_tensors(circuit, circuit_opt)
reduction = (original_gates - len(circuit_opt.gates)) / original_gates
return circuit_opt, reduction
Collision-free ion shuttle scheduling using linear programming and LDPC syndrome extraction for fault-tolerant quantum error correction.
using JuMP, HiGHS, LinearAlgebra
using SparseArrays
struct IonShuttleScheduler
trap_positions::Matrix{Float64}
shuttle_graph::SparseMatrixCSC{Bool}
ldpc_matrix::SparseMatrixCSC{Bool}
end
function schedule_ldpc_syndrome(scheduler::IonShuttleScheduler,
syndrome_rounds::Int)
model = Model(HiGHS.Optimizer)
n_ions = size(scheduler.trap_positions, 1)
n_timesteps = syndrome_rounds * 4 # QEC cycle length
# Binary variables for ion positions
@variable(model, x[1:n_ions, 1:n_timesteps], Bin)
# Collision avoidance constraints
for t in 1:n_timesteps
for pos in 1:size(scheduler.trap_positions, 1)
@constraint(model, sum(x[i, t] for i in 1:n_ions
if can_occupy(i, pos, t)) <= 1)
end
end
# LDPC syndrome extraction constraints
H = scheduler.ldpc_matrix
for round in 1:syndrome_rounds
t_start = (round - 1) * 4 + 1
# Ensure stabilizer measurements are collision-free
enforce_stabilizer_schedule!(model, x, H, t_start)
end
# Minimize total shuttle distance
@objective(model, Min, sum(shuttle_distance(i, t, x)
for i in 1:n_ions, t in 1:n_timesteps-1))
optimize!(model)
return value.(x)
end
Continuous performance validation through automated Metriq submissions, tracking quantum volume, fidelity benchmarks, and algorithmic performance metrics.
import requests
import json
from datetime import datetime
from typing import Dict, Any
class MetriqBenchmarkSubmitter:
def __init__(self, api_key: str, platform_name: str = "MEROPE"):
self.api_key = api_key
self.platform_name = platform_name
self.base_url = "https://metriq.info/api"
def submit_quantum_volume(self, qv_result: Dict[str, Any]):
"""Submit quantum volume benchmark to Metriq"""
submission = {
"task": "quantum-volume",
"platform": self.platform_name,
"method": "MEROPE Real-time QV",
"metric": "quantum_volume",
"metric_value": qv_result["quantum_volume"],
"confidence_interval": qv_result["confidence_95"],
"timestamp": datetime.now().isoformat(),
"metadata": {
"circuit_depth": qv_result["depth"],
"num_qubits": qv_result["qubits"],
"fidelity": qv_result["avg_fidelity"],
"shots": qv_result["shots"],
"error_mitigation": "real_time_calibration"
}
}
response = requests.post(
f"{self.base_url}/submissions",
headers={"Authorization": f"Bearer {self.api_key}"},
json=submission
)
return response.json()
def automated_benchmark_pipeline(self):
"""Run full benchmark suite and submit to Metriq"""
benchmarks = [
self.run_quantum_volume_benchmark(),
self.run_randomized_benchmarking(),
self.run_process_tomography(),
self.run_algorithm_benchmarks()
]
for benchmark in benchmarks:
result = self.submit_quantum_volume(benchmark)
print(f"Submitted {benchmark['task']}: {result['status']}")
Comprehensive REST and gRPC APIs for quantum computing control, telemetry ingestion, and real-time optimization.
Primary authentication method using JSON Web Tokens with 1-hour expiration and refresh token rotation.
# Obtain JWT token
POST /auth/login
Content-Type: application/json
{
"username": "quantum_researcher",
"password": "secure_password",
"mfa_token": "123456"
}
# Response
{
"access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
"refresh_token": "def502004a8b7e9c...",
"expires_in": 3600
}
# Use in subsequent requests
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Service-to-service authentication using scoped API keys with rate limiting and IP restrictions.
# API Key in header (recommended)
X-API-Key: mer_live_sk_1234567890abcdef
X-API-Secret: mer_secret_9876543210fedcba
# Or in query parameter (not recommended for production)
GET /api/v1/telemetry?api_key=mer_live_sk_1234567890abcdef
High-throughput telemetry data ingestion with batching support
{
"batch_id": "batch_2024_001",
"timestamp": "2024-01-15T10:30:00.123Z",
"events": [
{
"event_type": "gate_fidelity",
"qubit_id": "q0",
"gate_type": "cx",
"fidelity": 0.9987,
"duration_ns": 245,
"metadata": {
"temperature_mk": 15.2,
"laser_power_mw": 2.1
}
}
]
}
{
"status": "accepted",
"batch_id": "batch_2024_001",
"events_processed": 1,
"processing_time_ms": 12.4,
"next_batch_url": "/api/v1/telemetry/batch/batch_2024_002"
}
Live fidelity metrics with Bayesian confidence intervals
# Get current fidelity for specific qubits
GET /api/v1/fidelity/realtime?qubits=q0,q1,q2&time_window=300
# Response with 95% confidence intervals
{
"timestamp": "2024-01-15T10:30:15.456Z",
"fidelity_metrics": {
"q0": {
"mean_fidelity": 0.9987,
"confidence_95": [0.9962, 0.9996],
"sample_count": 1247,
"last_update": "2024-01-15T10:30:14.123Z"
}
},
"system_wide": {
"overall_fidelity": 0.9912,
"entanglement_rate_hz": 15234.7
}
}
Submit quantum circuits for ZX-calculus optimization
{
"circuit_qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[3];\ncreg c[3];\nh q[0];\ncx q[0],q[1];\nmeasure q -> c;",
"optimization_level": "aggressive",
"target_platform": "ion_trap",
"constraints": {
"max_depth": 50,
"preserve_entanglement": true
}
}
# Response
{
"job_id": "opt_job_987654321",
"status": "completed",
"optimization_results": {
"original_depth": 42,
"optimized_depth": 28,
"reduction_percentage": 33.3,
"gate_count_reduction": 15,
"estimated_fidelity": 0.9945
},
"optimized_qasm": "OPENQASM 2.0;\n..."
}
Retrieve automated Metriq benchmark results and historical data
GET /api/v1/benchmarks/results?benchmark_type=quantum_volume&start_date=2024-01-01
{
"benchmarks": [
{
"benchmark_id": "qv_20240115_001",
"benchmark_type": "quantum_volume",
"quantum_volume": 128,
"confidence_95": [121, 135],
"timestamp": "2024-01-15T09:00:00Z",
"circuit_depth": 8,
"qubits": 7,
"shots": 8192,
"metriq_submission_id": "metriq_987654"
}
],
"pagination": {
"page": 1,
"total_pages": 15,
"next_url": "/api/v1/benchmarks/results?page=2"
}
}
Configure real-time alerts for fidelity degradation, system failures, and benchmark thresholds.
# Register webhook endpoint
POST /api/v1/webhooks
{
"url": "https://your-app.com/merope-webhooks",
"events": ["fidelity_alert", "system_error", "benchmark_complete"],
"secret": "webhook_secret_key_abc123",
"filters": {
"min_severity": "warning",
"qubits": ["q0", "q1", "q2"]
}
}
# Webhook payload example
{
"event_type": "fidelity_alert",
"timestamp": "2024-01-15T10:35:00Z",
"severity": "critical",
"alert_data": {
"qubit_id": "q1",
"current_fidelity": 0.8945,
"threshold": 0.95,
"confidence_interval": [0.8821, 0.9069],
"suggested_action": "recalibrate_laser_power"
},
"signature": "sha256=a8b7c6d5e4f3..."
}
syntax = "proto3";
package merope.v1;
service QuantumTelemetryService {
// Streaming telemetry ingestion
rpc StreamTelemetry(stream TelemetryEvent) returns (stream TelemetryResponse);
// Real-time fidelity monitoring
rpc SubscribeFidelity(FidelitySubscription) returns (stream FidelityUpdate);
// Circuit optimization
rpc OptimizeCircuit(CircuitOptimizationRequest) returns (CircuitOptimizationResponse);
}
message TelemetryEvent {
string event_id = 1;
int64 timestamp_ns = 2;
string qubit_id = 3;
EventType event_type = 4;
map<string, double> measurements = 5;
map<string, string> metadata = 6;
}
message FidelityUpdate {
string qubit_id = 1;
double mean_fidelity = 2;
double confidence_lower = 3;
double confidence_upper = 4;
int32 sample_count = 5;
int64 timestamp_ns = 6;
}
import asyncio
import aiohttp
from merope_sdk import MeropeClient, TelemetryEvent, CircuitOptimizer
from merope_sdk.exceptions import MeropeAPIError, AuthenticationError
class QuantumExperiment:
def __init__(self, api_key: str, secret: str):
self.client = MeropeClient(
api_key=api_key,
api_secret=secret,
base_url="https://api.merope.quantum",
timeout=30.0
)
async def run_fidelity_monitoring(self):
"""Monitor real-time fidelity with automatic retry logic"""
try:
async with self.client.subscribe_fidelity(
qubits=["q0", "q1", "q2"],
update_interval_ms=100
) as fidelity_stream:
async for update in fidelity_stream:
if update.mean_fidelity < 0.95:
await self.handle_low_fidelity(update)
except AuthenticationError as e:
print(f"Authentication failed: {e}")
await self.refresh_credentials()
except MeropeAPIError as e:
if e.status_code == 429: # Rate limit
await asyncio.sleep(e.retry_after or 5)
return await self.run_fidelity_monitoring()
raise
async def optimize_and_submit_circuit(self, qasm_circuit: str):
"""Optimize circuit with comprehensive error handling"""
try:
optimizer = CircuitOptimizer(self.client)
# Submit for optimization
job = await optimizer.optimize_async(
circuit_qasm=qasm_circuit,
optimization_level="aggressive",
timeout=120
)
# Poll for completion with exponential backoff
result = await job.wait_for_completion(
poll_interval=2.0,
max_wait_time=300.0
)
if result.optimization_success:
print(f"Optimization reduced depth by {result.reduction_percentage}%")
return result.optimized_qasm
else:
print(f"Optimization failed: {result.error_message}")
return qasm_circuit # Return original on failure
except TimeoutError:
print("Circuit optimization timed out")
return qasm_circuit
except Exception as e:
print(f"Unexpected error: {e}")
raise
# Usage example
async def main():
experiment = QuantumExperiment(
api_key="mer_live_sk_1234567890abcdef",
secret="mer_secret_9876543210fedcba"
)
# Start fidelity monitoring
monitor_task = asyncio.create_task(experiment.run_fidelity_monitoring())
# Optimize a sample circuit
sample_qasm = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
creg c[3];
h q[0];
cx q[0],q[1];
cx q[1],q[2];
measure q -> c;
"""
optimized = await experiment.optimize_and_submit_circuit(sample_qasm)
print(f"Optimized circuit: {optimized}")
if __name__ == "__main__":
asyncio.run(main())
use merope_sdk::{MeropeClient, TelemetryEvent, Error as MeropeError};
use tokio::time::{sleep, Duration, timeout};
use anyhow::{Result, Context};
#[derive(Clone)]
pub struct QuantumController {
client: MeropeClient,
retry_config: RetryConfig,
}
#[derive(Clone)]
struct RetryConfig {
max_retries: u32,
base_delay_ms: u64,
max_delay_ms: u64,
}
impl QuantumController {
pub fn new(api_key: String, secret: String) -> Self {
Self {
client: MeropeClient::builder()
.api_key(api_key)
.api_secret(secret)
.base_url("https://api.merope.quantum")
.timeout(Duration::from_secs(30))
.build(),
retry_config: RetryConfig {
max_retries: 3,
base_delay_ms: 1000,
max_delay_ms: 30000,
}
}
}
pub async fn ingest_telemetry_with_retry(
&self,
events: Vec<TelemetryEvent>
) -> Result<()> {
let mut attempts = 0;
loop {
match self.client.ingest_telemetry(&events).await {
Ok(response) => {
println!("Successfully ingested {} events", response.events_processed);
return Ok(());
}
Err(MeropeError::RateLimit { retry_after }) => {
if attempts >= self.retry_config.max_retries {
return Err(anyhow::anyhow!("Max retries exceeded for rate limit"));
}
let delay = retry_after.unwrap_or(Duration::from_secs(5));
println!("Rate limited, retrying in {:?}", delay);
sleep(delay).await;
attempts += 1;
}
Err(MeropeError::Network(e)) => {
if attempts >= self.retry_config.max_retries {
return Err(anyhow::anyhow!("Network error after {} retries: {}",
attempts, e));
}
let delay = Duration::from_millis(
self.retry_config.base_delay_ms * 2_u64.pow(attempts)
).min(Duration::from_millis(self.retry_config.max_delay_ms));
println!("Network error, retrying in {:?}: {}", delay, e);
sleep(delay).await;
attempts += 1;
}
Err(e) => {
return Err(anyhow::anyhow!("Unrecoverable error: {}", e));
}
}
}
}
pub async fn real_time_fidelity_monitor(&self) -> Result<()> {
let mut stream = self.client
.subscribe_fidelity(&["q0", "q1", "q2"])
.await
.context("Failed to create fidelity subscription")?;
while let Some(update) = stream.next().await {
match update {
Ok(fidelity_data) => {
if fidelity_data.mean_fidelity < 0.95 {
self.handle_fidelity_alert(&fidelity_data).await?;
}
}
Err(MeropeError::ConnectionLost) => {
println!("Connection lost, attempting to reconnect...");
sleep(Duration::from_secs(1)).await;
// Recreate stream
stream = self.client
.subscribe_fidelity(&["q0", "q1", "q2"])
.await
.context("Failed to reconnect fidelity stream")?;
}
Err(e) => {
eprintln!("Fidelity stream error: {}", e);
return Err(anyhow::anyhow!("Stream error: {}", e));
}
}
}
Ok(())
}
async fn handle_fidelity_alert(&self, data: &FidelityData) -> Result<()> {
println!("ALERT: Low fidelity detected on {}: {:.4}",
data.qubit_id, data.mean_fidelity);
// Implement automatic recalibration trigger
let recalibration_result = timeout(
Duration::from_secs(60),
self.client.trigger_recalibration(&data.qubit_id)
).await;
match recalibration_result {
Ok(Ok(_)) => println!("Recalibration initiated for {}", data.qubit_id),
Ok(Err(e)) => eprintln!("Recalibration failed: {}", e),
Err(_) => eprintln!("Recalibration request timed out"),
}
Ok(())
}
}
#[tokio::main]
async fn main() -> Result<()> {
let controller = QuantumController::new(
"mer_live_sk_1234567890abcdef".to_string(),
"mer_secret_9876543210fedcba".to_string(),
);
// Example telemetry events
let events = vec![
TelemetryEvent::new("q0", "gate_fidelity", 0.9987, None),
TelemetryEvent::new("q1", "gate_fidelity", 0.9962, None),
];
// Ingest telemetry with automatic retry
controller.ingest_telemetry_with_retry(events).await?;
// Start real-time monitoring
controller.real_time_fidelity_monitor().await?;
Ok(())
}
using HTTP, JSON3, Dates, Logging
using MeropeSDK: MeropeClient, TelemetryEvent, CircuitOptimizer
using MeropeSDK: AuthenticationError, RateLimitError, NetworkError
struct QuantumSimulation
client::MeropeClient
retry_config::NamedTuple
end
function QuantumSimulation(api_key::String, secret::String)
client = MeropeClient(
api_key=api_key,
api_secret=secret,
base_url="https://api.merope.quantum",
timeout=30.0
)
retry_config = (
max_retries=3,
base_delay=1.0,
max_delay=30.0,
backoff_factor=2.0
)
QuantumSimulation(client, retry_config)
end
function ingest_telemetry_robust(sim::QuantumSimulation, events::Vector{TelemetryEvent})
for attempt in 1:sim.retry_config.max_retries
try
response = MeropeSDK.ingest_telemetry(sim.client, events)
@info "Successfully ingested $(response.events_processed) events"
return response
catch e
if e isa RateLimitError
delay = something(e.retry_after, 5.0)
@warn "Rate limited, waiting $(delay)s before retry"
sleep(delay)
continue
elseif e isa NetworkError
if attempt == sim.retry_config.max_retries
@error "Network error after $(attempt) attempts: $(e.message)"
rethrow(e)
end
delay = min(
sim.retry_config.base_delay * sim.retry_config.backoff_factor^(attempt-1),
sim.retry_config.max_delay
)
@warn "Network error (attempt $attempt), retrying in $(delay)s: $(e.message)"
sleep(delay)
continue
elseif e isa AuthenticationError
@error "Authentication failed: $(e.message)"
rethrow(e)
else
@error "Unexpected error: $e"
rethrow(e)
end
end
end
error("Max retries exceeded")
end
function monitor_fidelity_stream(sim::QuantumSimulation, qubits::Vector{String})
subscription = MeropeSDK.subscribe_fidelity(sim.client, qubits, update_interval_ms=100)
try
for update in subscription
try
if update.mean_fidelity < 0.95
handle_low_fidelity_alert(sim, update)
end
# Log current status
@info "Fidelity update: $(update.qubit_id) = $(round(update.mean_fidelity, digits=4))"
catch e
@error "Error processing fidelity update: $e"
continue
end
end
catch e
if e isa NetworkError
@warn "Fidelity stream disconnected, attempting reconnection..."
sleep(2.0)
# Recursive reconnection with exponential backoff
return monitor_fidelity_stream(sim, qubits)
else
@error "Fatal error in fidelity monitoring: $e"
rethrow(e)
end
finally
close(subscription)
end
end
function handle_low_fidelity_alert(sim::QuantumSimulation, update)
@warn "LOW FIDELITY ALERT: $(update.qubit_id) fidelity = $(update.mean_fidelity)"
# Trigger automatic recalibration
try
recalibration_job = MeropeSDK.trigger_recalibration(sim.client, update.qubit_id)
@info "Recalibration initiated for $(update.qubit_id): job_id = $(recalibration_job.job_id)"
# Optional: Wait for completion
result = MeropeSDK.wait_for_job(sim.client, recalibration_job.job_id, timeout=60.0)
if result.status == "completed"
@info "Recalibration completed successfully"
else
@warn "Recalibration failed: $(result.error_message)"
end
catch e
@error "Failed to trigger recalibration: $e"
end
end
function optimize_circuit_with_fallback(sim::QuantumSimulation, qasm_circuit::String)
optimizer = CircuitOptimizer(sim.client)
try
# Submit optimization job
job = MeropeSDK.optimize_circuit_async(
optimizer,
qasm_circuit,
optimization_level="aggressive",
timeout=120.0
)
@info "Circuit optimization submitted: job_id = $(job.job_id)"
# Wait for completion with progress monitoring
result = MeropeSDK.wait_for_completion(
job,
poll_interval=2.0,
max_wait_time=300.0,
progress_callback=job_progress -> @info "Optimization progress: $(job_progress.percentage)%"
)
if result.optimization_success
reduction = result.reduction_percentage
@info "Circuit optimization completed: $(reduction)% depth reduction"
return result.optimized_qasm
else
@warn "Circuit optimization failed: $(result.error_message)"
return qasm_circuit # Return original circuit
end
catch e
if e isa TimeoutError
@warn "Circuit optimization timed out, using original circuit"
return qasm_circuit
else
@error "Circuit optimization error: $e"
rethrow(e)
end
end
end
# Usage example
function main()
# Initialize quantum simulation
sim = QuantumSimulation(
"mer_live_sk_1234567890abcdef",
"mer_secret_9876543210fedcba"
)
# Create sample telemetry events
events = [
TelemetryEvent("q0", "gate_fidelity", 0.9987, Dict("gate_type" => "cx")),
TelemetryEvent("q1", "gate_fidelity", 0.9962, Dict("gate_type" => "h")),
TelemetryEvent("q2", "gate_fidelity", 0.9943, Dict("gate_type" => "cx"))
]
# Ingest telemetry with robust error handling
try
ingest_telemetry_robust(sim, events)
catch e
@error "Failed to ingest telemetry: $e"
end
# Start background fidelity monitoring
@async monitor_fidelity_stream(sim, ["q0", "q1", "q2"])
# Optimize a quantum circuit
sample_qasm = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
creg c[3];
h q[0];
cx q[0],q[1];
cx q[1],q[2];
measure q -> c;
"""
optimized_circuit = optimize_circuit_with_fallback(sim, sample_qasm)
@info "Circuit optimization complete"
# Keep monitoring running
@info "Quantum simulation initialized and monitoring active"
return sim
end
# Run the example
if abspath(PROGRAM_FILE) == @__FILE__
main()
end
High-performance streaming at 10k+ events/second with sub-10ms latency using async I/O and Prometheus metrics.
Live fidelity tracking with Beta-binomial models updating every second, providing real-time confidence intervals.
Automated circuit rewriting achieving 25-33% depth reduction on AQ-32 class circuits with identity stabilization.
Advanced quantum computing research and development environment featuring real-time optimization, Bayesian inference, and distributed quantum control systems.
Primary Objective: Develop and validate quantum computing control algorithms
that maintain 99%+ fidelity across distributed quantum hardware networks,
enabling fault-tolerant quantum computation at scale.
Key Research Areas:
• Real-time Bayesian parameter estimation for quantum gates
• ZX-calculus circuit optimization with >25% depth reduction
• Ion shuttle scheduling with collision-free path planning
• Photonic interconnect fidelity preservation across racks
• Automated benchmarking and performance validation
High-performance data ingestion system processing 10,000+ quantum events per second with sub-millisecond latency using async I/O and zero-copy serialization.
Real-time quantum gate fidelity estimation using hierarchical Beta-Binomial models with 95% confidence intervals, updating every second.
Graph-theoretic circuit optimization achieving 25-33% depth reduction on AQ-32 class circuits using PyZX rewrite rules and Clifford+T synthesis.
Collision-free ion movement optimization using linear programming and LDPC syndrome extraction for fault-tolerant quantum error correction.
Continuous performance validation through automated Metriq submissions tracking quantum volume, randomized benchmarking, and algorithmic performance.
Research into maintaining >99% fidelity across distributed quantum hardware using adaptive error correction and real-time channel compensation.
MEROPE thrives on open collaboration. Join our vibrant community of quantum researchers, developers, and enthusiasts shaping the future of quantum computing infrastructure.
Our open-source ecosystem is organized across multiple repositories for maximum modularity and contribution clarity:
merope-platform/
├── merope-core/ # Core quantum platform engine
│ ├── src/quantum/ # Quantum circuit optimization
│ ├── src/telemetry/ # Real-time telemetry system
│ ├── src/bayesian/ # Bayesian inference models
│ └── tests/ # Comprehensive test suite
├── merope-api/ # REST & gRPC API server
│ ├── proto/ # Protocol buffer definitions
│ ├── handlers/ # HTTP request handlers
│ └── middleware/ # Authentication & rate limiting
├── merope-sdk/ # Multi-language SDKs
│ ├── python/ # Python SDK & PyMC models
│ ├── rust/ # High-performance Rust SDK
│ ├── julia/ # Scientific computing SDK
│ └── examples/ # SDK usage examples
├── merope-ui/ # Web dashboard & visualization
├── merope-docs/ # Documentation & tutorials
├── merope-benchmarks/ # Performance benchmarking suite
└── merope-contrib/ # Community contributions & plugins
make dev-setupcargo test && python -m pytestFocus: Platform architecture, performance optimization, and quantum algorithm integration
Participants: Core maintainers, algorithm researchers, and performance engineers
Focus: Open Q&A, contribution guidance, and academic collaboration discussions
Open to: All community members, students, researchers, and industry partners
Our community drives MEROPE's development through transparent, weighted voting on GitHub Discussions:
Community members submit feature requests with technical specifications and use case justifications
Core team evaluates feasibility, provides complexity estimates, and suggests implementation approaches
Weighted voting based on contribution history, domain expertise, and community standing
Top-voted features integrated into quarterly development milestones with clear timelines
MEROPE is proudly supported by Unitary Fund's microgrant program, enabling accelerated development of open quantum computing infrastructure.
We help community members apply for Unitary Fund microgrants to extend MEROPE's capabilities. Previous successful applications include noise characterization tools, quantum error correction protocols, and educational visualization components.
Collaborative research on superconducting qubit characterization and real-time error correction
Ion trap optimization algorithms and Bayesian parameter estimation techniques
Quantum error correction code integration and fault-tolerant algorithm development
12-month fellowships for PhD students working on quantum optimization, Bayesian inference, or hardware integration projects
10-week intensive programs focusing on SDK development, quantum algorithm implementation, and educational content creation
ZX-calculus simplification, variational quantum algorithms, and quantum approximate optimization
Hierarchical models for quantum parameter estimation, uncertainty quantification, and adaptive experimental design
Real-time noise modeling, device parameter drift tracking, and automated recalibration protocols
Interested in partnering with MEROPE for your quantum computing research? We provide computational resources, technical expertise, and publication opportunities.
Whether you're a student, researcher, or industry professional, there's a place for you in the MEROPE community.