Exchange-only quantum computation is a version of spin-based quantum computation that entirely avoids the difficulty of controlling individual spins by a magnetic field and instead functions by sequences of exchange pulses. The challenge for exchange-only quantum computation is to find short sequences that generate the required logical quantum gates. A reduction of the total gate time of such synthesized quantum gates can help to minimize the effects of decoherence and control errors during the gate operation and thus increase the total gate fidelity. We apply reinforcement learning to the optimization of exchange-gate sequences realizing the CNOT and CZ two-qubit gates which lend themselves to the construction of universal gate sets for quantum computation. We obtain a significant improvement regarding the total gate time compared to previously known results.
Exchange-only quantum computation is a version of spin-based quantum computation that entirely avoids the difficulty of controlling individual spins by a magnetic field and instead functions by sequences of exchange pulses. The challenge for exchange-only quantum computation is to find short sequences that generate the required logical quantum gates. A reduction of the total gate time of such synthesized quantum gates can help to minimize the effects of decoherence and control errors during the gate operation and thus increase the total gate fidelity. We apply reinforcement learning to the optimization of exchange-gate sequences realizing the CNOT and CZ two-qubit gates which lend themselves to the construction of universal gate sets for quantum computation. We obtain a significant improvement regarding the total gate time compared to previously known results.
Abstract Exchange-only quantum computation is a version of spin-based quantum computation that entirely avoids the difficulty of controlling individual spins by a magnetic field and instead functions by sequences of exchange pulses. The challenge for exchange-only quantum computation is to find short sequences that generate the required logical quantum gates. A reduction of the total gate time of such synthesized quantum gates can help to minimize the effects of decoherence and control errors during the gate operation and thus increase the total gate fidelity. We apply reinforcement learning to the optimization of exchange-gate sequences realizing the CNOT and CZ two-qubit gates which lend themselves to the construction of universal gate sets for quantum computation. We obtain a significant improvement regarding the total gate time compared to previously published results.
Fast quantum gates are crucial not only for the contemporary era of noisy intermediate-scale quantum devices but also for the prospective development of practical fault-tolerant quantum computing. Leakage errors, which arise from data qubits jumping beyond the confines of the computational subspace, are the main challenges in realizing nonadiabatically driven, fast gates. In this work, we propose and illustrate the usefulness of reinforcement learning (RL) to generate fast two-qubit gates in practical multilevel superconducting qubits. In particular, we show that the RL controller offers great effectiveness in finding piecewise constant gate-pulse sequences that act on two transmon data qubits coupled by a tunable coupler to generate a controlled-Z (cz) gate with a gate time of 10 ns and an error rate of approximately $4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$. Using a gradient-based method to solve the same optimization problem often does not achieve high fidelity for such fast gates. However, we show that using the gate pulses discovered by RL as an ansatz for the gradient-based controller can substantially enhance fidelity compared to using RL alone. While for a 10-ns pulse, this improvement is marginal, the combined RL + gradient approach decreases the gate errors below ${10}^{\ensuremath{-}4}$ for a gate of length 20 ns.
Current experimental quantum computing devices are limited by noise, mainly originating from entangling gates. If an efficient gate sequence for an operation is unknown, one often employs layered parameterized quantum circuits, especially hardware-efficient ansätze, with fixed entangling layer structures. We demonstrate a reinforcement learning algorithm to improve on these by optimizing the entangling gate sequence in the task of quantum state preparation. This allows us to restrict the required number of CNOT gates while taking the qubit connectivity architecture into account. Recent advancements using reinforcement learning have already demonstrated the power of this technique when optimizing the circuit for a sequence of non-parameterized gates. We extend this approach to parameterized gate sets by incorporating general single-qubit unitaries, thus allowing us to consistently reach higher state preparation fidelities at the same number of CNOT gates compared to a hardware-efficient ansatz.
In light of the rapid advancements in quantum computing, the synthesis of quantum circuits has drawn significant attention. Quantum circuits are required to be reversible owing to their inherent physical characteristics. However, synthesizing such circuits and optimizing their performance present substantial challenges. In this paper, we proposed to adopt reinforcement learning for synthesizing the reversible circuits involving NOT, CNOT, Toffoli, and other relevant gates and, in doing so, minimize the quantum computational costs of the resulting circuits. The experimental results demonstrated that the reversible circuits generated using the proposed method outperform the prior methods in both the gate counts and the total cost.
Quantum circuit optimization is a central task in quantum computing, as current NISQ devices suffer from error propagation that often scales with the number of operations. Among quantum operations, the CNOT gate is of fundamental importance, being the only two-qubit gate in the universal Clifford+T set. Minimizing CNOT gates is therefore particularly important, as these gates exhibit higher error rates and they typically introduce additional routing overhead due to hardware constraints. The problem of CNOT gate minimization has been addressed by heuristic algorithms such as the well-known Patel-Markov-Hayes (PMH) for linear reversible synthesis (i.e., CNOT minimization with no topological constraints), and more recently by Reinforcement Learning (RL) based strategies in the more complex case of topology-aware synthesis, where each CNOT can act on a subset of qubit pairs. We introduce AlphaCNOT, an RL framework based on Monte Carlo Tree Search that effectively addresses the CNOT minimization problem by modeling it as a planning problem. In contrast to other RL-based solutions, our method is model-based, i.e., it can leverage lookahead search to evaluate future trajectories, thus finding more efficient sequences of CNOTs. Our method achieves a reduction of up to 24.62% in CNOT count compared with the PMH baseline on linear reversible synthesis, and a consistent gate count reduction on different topologies with up to 8 qubits, also with respect to state-of-the-art RL-based solutions. Our results suggest the combination of RL with search-based strategies can be applied to different circuit optimization tasks, thus fostering the transition toward the "quantum utility" era.
The design of high-fidelity quantum gates is difficult because it requires the optimization of two competing effects, namely maximizing gate speed and minimizing leakage out of the qubit subspace. We propose a deep reinforcement learning algorithm that uses two agents to address the speed and leakage challenges simultaneously. The first agent constructs the qubit in-phase control pulse using a policy learned from rewards that compensate short gate times. The rewards are obtained at intermediate time steps throughout the construction of a full-length pulse, allowing the agent to explore the landscape of shorter pulses. The second agent determines an out-of-phase pulse to target leakage. Both agents are trained on real-time data from noisy hardware, thus providing model-free gate design that adapts to unpredictable hardware noise. To reduce the effect of measurement classification errors, the agents are trained directly on the readout signal from probing the qubit. We present proof-of-concept experiments by designing X and square root of X gates of various durations on IBM hardware. After just 200 training iterations, our algorithm is able to construct novel control pulses up to two times faster than the default IBM gates, while matching their performance in terms of state fidelity and leakage rate. As the length of our custom control pulses increases, they begin to outperform the default gates. Improvements to the speed and fidelity of gate operations open the way for higher circuit depth in quantum simulation, quantum chemistry and other algorithms on near-term and future quantum devices.