Achieving utility-scale quantum execution with Fire Opal
At Q-CTRL, we build infrastructure software that empowers enterprises, developers, and researchers to get the most out of quantum hardware. With the era of advantage on the horizon, Fire Opal’s latest updates enable the reliable execution of larger, higher-complexity circuits on today’s machines and support workloads as machines evolve.
Achieving utility-scale quantum computing requires more than raw qubit count alone. Breakthrough results increasingly depend on the deployment of smarter, more dynamic algorithms combining quantum operations with classical control flows. Equally critical is the ability to successfully execute such circuits with richer logic on noisy devices.
Furthermore, as quantum hardware evolves to support such non-linear programs, complexity increases. Circuits get larger, payloads get heavier, so better visibility into execution is needed.
To meet these requirements, Fire Opal now supports two core capabilities of modern circuit design, alongside two user-requested expanded capabilities for scale and visibility:
- Mid-circuit measurement to enable dynamic workflows and conditional logic within a single execution.
- Multiple quantum and classical registers to support richer circuit structure and mixed quantum-classical computation.
- Larger payload support to allow execution of increasingly complex, high-width and high-depth circuits.
- Circuit metadata to provide insights into key metrics such as circuit depth and estimated duration.
These features give users greater flexibility to solve larger, more complex problems with more sophisticated circuit logic. Best of all, they are integrated directly into Fire Opal’s execute function, allowing you to leverage modern circuit execution techniques while suppressing errors in the background.
Take the first step towards real-time quantum compute with mid-circuit measurement
Until now, quantum circuit execution has largely been fixed, with upfront definition followed by execution without consideration of the status or intermediate outcomes. As quantum algorithm design advances, this static approach is shifting towards more dynamic computation.
Hardware providers like IBM Quantum and Quantinuum have enabled mid-circuit measurement on their devices, allowing users to check the state of an individual qubit while executing a broader circuit.
At the fundamental level, mid-circuit measurement provides a way to influence the state of one or more qubits, similar to performing a gate operation. Since measurements are projective operations, the measurement of one qubit will affect the others if a qubit is entangled with other qubits.
For example, imagine N+1 qubits are in an equal superposition of |,0> and |,1> where |> and |> are N-qubit states. Measuring the last qubit in the state 1 is equivalent to transforming the state of the other N-qubits from an equal superposition of |> and |> to the state |>.
import qiskit
From qiskit.qasm2 import dumps
circuit = qiskit.QuantumCircuit(1, 2)
circuit.h(0)
circuit.measure(0, 0)
circuit.x(0)
circuit.measure(0, 1)
circuit_qasm = dumps(circuit)
print(circuit).png)
Mid-circuit measurement is crucial to implementing some algorithms involving feedback, such as quantum error correction (QEC). Most implementations of QEC require repeated measurements (stabilizer checks) of ancilla qubits, which encode information about errors that occurred during circuit execution. These checks allow for the real-time identification and correction of errors. The same principle can be applied more broadly by using mid-circuit measurement to identify errors and filtering out results based on a method called post-selection. In our documentation, you can find a guide on how to create a circuit with mid-circuit measurements and run a tutorial on how to apply mid-circuit measurements to execute parity checks. Parity checks are an important building block in many QEC protocols.
Mid-circuit measurement, in combination with qubit reset, also provides the opportunity to leverage devices more efficiently by reusing qubits. Since today’s quantum hardware is limited by the number of qubits, the potential to use mid-circuit measurement with qubit reset is key to condensing larger circuits to work on smaller devices and maximizing the potential of the NISQ-era devices available today.
Now you can execute quantum algorithms built with calls to mid-circuit-measurement operations, expanding the art of the possible while still benefiting from the extraordinary power of Fire Opal’s error suppression technology.
Organize your circuits using multiple quantum and classical registers
Quantum circuits consist of classical and quantum registers, which represent blocks of bits and qubits, respectively. Organizing the blocks into discrete units allows for a more intuitive algorithm design, particularly useful as circuits become more complex to match utility-scale devices. Defining multiple registers is one way to write circuits that are easier to understand and visualize by organizing qubits and their measured values into groups based on their purposes.
Storing information in multiple quantum registers is particularly helpful when certain qubits have a distinct purpose, and some operations are only performed on a subset of qubits. QEC is a canonical example where physical qubits are used for two purposes: to encode logical states and to measure, store, and correct errors. In cases like this, the different types of qubits (sometimes known as data vs ancilla qubits) can be stored in multiple distinct quantum registers.
Classical registers in the context of quantum circuits are used to store the measured values of qubits which can help with post-processing tasks. It also becomes relevant in the context of mid-circuit measurement, since the intermediate values resulting from these measurements can be stored in a separate register.
Now, any algorithm fed into Fire Opal can have multiple quantum and classical registers defined. As an example, we leveraged multiple classical registers in our implementation of support for mid-circuit measurement to store the readout values to be post-processed and returned. In our documentation, you will find guides for both how to create circuits with multiple classical and quantum registers and how to use them for mid-circuit measurements.
Run larger payloads for utility-scale
To enable you to execute these increasingly complex, high-width and high-depth circuits without bottlenecks, we have removed the payload size limit, enabling larger payload support.
The introduction of multiple registers and mid-circuit measurements gives rise to an underappreciated growth in the necessary metadata required to execute a circuit. Just as we’ve seen in compilation, trying to execute more complex applications on growing QPUs can lead to some surprise bottlenecks; it’s easy to be hindered by frameworks or IDEs whose data-transmission limits just can’t handle the simple definition of such complex circuits. By supporting larger payloads, Fire Opal now enables you to process high-depth algorithms and complex classical-quantum feedback loops in a single execution request. This ensures that even your most data-intensive designs are seamlessly transmitted and optimized at scale.
Ultimately, this update removes the technical ceiling for developers moving beyond small-scale prototypes. Whether you are running a massive quantum phase estimation (QPE) algorithm, a sampling-intensive sample-based quantum diagonalization (SQD) routine, or a multi-stage QEC protocol, the increased payload support allows you to focus on the logic of your design rather than the constraints of the data pipeline.
Get insights with circuit metadata
As algorithms grow in complexity, visibility into circuit execution becomes essential for validating outputs and refining circuits. Fire Opal results now include circuit metadata that enables users to verify details that provide a system snapshot at time of execution: backend used, shot count, execution timestamp, circuit depth and estimated duration, gate counts, and more.
In doing so, we provide execution feedback and insights, such as seeing that a circuit was compressed to one-fifth of its original depth (as demonstrated by our partner Mitsubishi Chemical), while allowing you to benchmark performance against hardware coherence times to ensure your designs are tailored for successful, low-noise execution. Learn more in our documentation.
Below is an example of circuit metadata output:
"circuit_metadata": [
{
"layout": [
1,
2
],
"depth": 10,
"estimated_duration": 5.8737777777777776e-06,
"shot_count": 128,
"t1_times": {
"1": 0.0001346041833294333,
"2": 0.00013778856170509223
},
"t2_times": {
"1": 2.7523891700892928e-05,
"2": 2.0275851970240523e-05
},
"single_qubit_gate_error": {
"1": {
"id": 0.00018883364189831766,
"rz": 0.0,
"sx": 0.00018883364189831766,
"x": 0.00018883364189831766
},
"2": {
"id": 0.00028611963491960706,
"rz": 0.0,
"sx": 0.00028611963491960706,
"x": 0.00028611963491960706
}
},
"two_qubit_gate_error": {
"(1,2)": {
"cx": 0.011271909136120584
}
},
"gate_count": {
"rz": 9,
"delay": 6,
"sx": 5,
"measure": 2,
"cx": 1,
"barrier": 1
}
}
]Build and run more complex algorithms today
The combination of mid-circuit measurement, multiple quantum and classical register support, and expanded payload capacity enables you to move beyond limits and into the era of quantum utility. Fire Opal ensures that as your algorithms grow in complexity, execution remains seamless with automated error suppression and optimized performance across different hardware backends.
Fire Opal is designed to support all algorithms, backends, and devices so that you can achieve the best possible results at any scale. By continuously expanding support for advanced device capabilities and removing data constraints, we are ensuring that the path from design to execution is easier than ever.
Start exploring these new capabilities today and see how Fire Opal can help you execute sophisticated, high-depth quantum algorithms with confidence.
Sign up for Fire Opal, visit our documentation for a full tutorial notebook, or get in touch with our expert team to learn more.
