We develop a method to synthesize a class of entangling multi-qubit gates for a quantum computing platform with fixed Ising-type interaction with all-to-all connectivity. The only requirement on the flexibility of the interaction is that it can be switched on and off for individual qubits. Our method yields a time-optimal implementation of the multi-qubit gates. We numerically demonstrate that the total multi-qubit gate time scales approximately linear in the number of qubits. Using this gate synthesis as a subroutine, we provide compilation strategies for important use cases: (i) we show that any Clifford circuit on n qubits can be implemented using at most 2 n multi-qubit gates without requiring ancilla qubits, (ii) we decompose the quantum Fourier transform in a similar fashion, (iii) we compile a simulation of molecular dynamics, and (iv) we propose a method for the compilation of diagonal unitaries with time-optimal multi-qubit gates, as a step towards general unitaries. As motivation, we provide a detailed discussion on a microwave controlled ion trap architecture with magnetic gradient induced coupling (MAGIC) for the generation of the Ising-type interactions.
We present a method for optimizing quantum circuit compilation by automating the allocation of auxiliary qubits for multi-qubit gate decompositions. This approach is implemented and evaluated within the high-level quantum programming platform Ket. Our results indicate that the decomposition of multi-qubit gates is more effectively handled by the compiler, which has access to all circuit parameters, rather than through a quantum programming API. To evaluate the approach, we compared our implementation against Qiskit, a widely used quantum programming platform, by analyzing two quantum algorithms. Using a 16-qubit QPU, we observed a reduction of 87% in the number of CNOT gates in Grover's algorithm for 9 qubits. For a state preparation algorithm with 7 qubits, the number of CNOT gates was reduced from
2.8×107 to
5.7×103, leveraging additional Ket optimizations for high-level quantum program constructions. Overall, a quadratic reduction in the number of CNOT gates in the final circuit was observed, with greater improvements achieved when more auxiliary qubits were available. These findings underscore the importance of automatic resource management, such as auxiliary qubit allocation, in optimizing quantum applications and improving their suitability for near-term quantum hardware.
Quantum computing hardware is affected by quantum noise that undermine the quality of results of an executed quantum program. Amongst other quantum noises, coherent error that caused by parameter drifting and miscalibration, remains critical. While coherent error mitigation has been studied before, studies focused either on gate-level or pulse-level -- missing cross-level optimization opportunities; And most of them only target single-qubit gates -- while multi-qubit gates are also used in practice. To address above limitations, this work proposes a cross-layer approach for coherent error mitigation that considers program-level, gate-level, and pulse-level compiler optimizations, by leveraging the hidden inverse theory, and exploiting the structure inside different quantum programs, while also considering multi-qubit gates. We implemented our approach as compiler optimization passes, and integrated into IBM Qiskit framework. We tested our technique on real quantum computer (IBM-Brisbane), and demonstrated up to 92% fidelity improvements (45% on average), on several benchmarks.