Decoherence Suppression by parallelization of quantum circuits

Roy Kallol, R. Srikanth, Talabattula Srinivas

Decoherence as an obstacle in quantum computation is viewed as a struggle between two forces [1]: the computation which uses the exponential dimension of Hilbert space, and decoherence which destroys this entanglement by collapse. In this model of decohered quantum computation, a sequential quantum computer loses the battle, because at each time step, only a local operation is carried out but g*(t) number of gates collapse. With quantum circuits computing in parallel way the situation is different- g(t) number of gates can be applied at each time step and number gates collapse because of decoherence. As g(t) ≈ g*(t) competition here is even [1]. Our paper improves on this model by slowing down g*(t) by encoding the circuit in parallel computing architectures and running it in Single Instruction Multiple Data (SIMD) paradigm. We have proposed a parallel ion trap architecture for single-bit rotation of a qubit.

Bin-Han Lu, Peng Wang, Zhao‐Yun Chen +5 more

Crosstalk poses a significant challenge in quantum computing, particularly when quantum gates are executed in parallel, as qubit frequency resonance can lead to residual coupling and reduced gate fidelity. Current solutions struggle to mitigate both crosstalk and decoherence during parallel two-qubit gate operations on frequency-tunable quantum chips. To address this, we propose a crosstalk-aware mapping and gate scheduling (CAMEL) approach, designed to mitigate crosstalk and suppress decoherence by leveraging the tunable coupler’s physical properties and incorporating a pulse compensation technique. CAMEL operates within a two-step compilation framework: first, a qubit mapping strategy that considers both crosstalk and decoherence; and second, a gate timing scheduling method that prioritizes the execution of the largest possible set of crosstalk-free parallel gates, reducing overall circuit execution time. Evaluation results demonstrate CAMEL’s superior ability to mitigate crosstalk compared to crosstalk-agnostic methods, while successfully suppressing decoherence where other approaches fail. Additionally, CAMEL performs better than dynamic-frequency-aware techniques, particularly in low-complexity hardware environments.

Pablo Paz Juan

Dorit Aharonov, Michael Ben-Or

We define formally decohered quantum computers (using density matrices), and present a simulation of them by a probabalistic classical Turing Machine. We study the slowdown of the simulation for two cases: (1) sequential quantum computers, or quantum Turing machines(QTM), and (2) parallel quantum computers, or quantum circuits. This paper shows that the computational power of decohered quantum computers depends strongly on the amount of parallelism in the computation. The expected slowdown of the simulation of a QTM is polynomial in time and space of the quantum computation, for any non zero decoherence rate. This means that a QTM subjected to any amount of noise is worthless. For decohered quantum circuits, the situation is more subtle and depends on the decoherence rate, eta. We find that our simulation is efficient for circuits with decoherence rate higher than some constant, but exponential for general circuits with decoherence rate lower than some other constant. Using computer experiments, we show that the transition from exponential cost to polynomial cost happens in a short range of decoherence rates, and exhibit the phase transitions in various quantum circuits.

M. Dugić, J. Jeknić-Dugić