Quantum computing has been celebrated for an extended period as the forthcoming advancement in computational capabilities, promising to tackle challenges that classical computers find insurmountable. Yet, the path to harnessing this promise is riddled with obstacles, particularly in terms of mitigating the intrinsic noise present in quantum systems. In a recent dialogue, Sergio Boixo, an influential player in Google’s quantum computing initiatives, highlighted the necessity of recognizing and addressing these issues, especially through the implementation of benchmarks that assess the capacities of modern noisy quantum processors.
### The Significance of Random Quantum Circuits
Central to this conversation is the role of random quantum circuits as a yardstick for evaluating quantum computing efficacy. Although the creation of genuinely random bit strings might cater to specific niche uses, Boixo emphasized that the true merit of this benchmark resides in assessing the noise thresholds that quantum algorithms can withstand. Essentially, random quantum circuits serve as a stress test for quantum processors, curated to push them to their extremes and evaluate their resilience to noise.
This is vital since quantum computers, particularly in the current noisy intermediate-scale quantum (NISQ) phase, are extremely prone to errors. Noise within quantum systems can stem from a multitude of sources, such as imperfect qubit control, environmental disturbances, and decoherence. By employing random quantum circuits, researchers can measure the extent of noise a quantum processor can handle before its operations become unreliable.
### The Benchmark as a Threshold
Boixo’s perspective is unequivocal: before quantum computers can address more intricate and beneficial challenges, they need to first excel in this benchmark. “Before you can address any additional applications, you must first triumph on this benchmark,” Boixo articulated. “If you do not succeed on this benchmark, then you are not succeeding on any subsequent benchmarks. This is the simplest task for a noisy quantum computer as compared to a supercomputer.”
This remark emphasizes the significance of mastering fundamental concepts prior to pursuing loftier aspirations. If a quantum processor cannot surpass classical computers in this relatively straightforward endeavor, it is doubtful it will excel in more complicated applications. In this regard, the benchmark acts as a litmus test for the practicality of quantum computing in its present configuration.
### Phase Transitions and Noise Resilience
A crucial takeaway from Boixo’s dialogue is the notion of a “phase transition” within quantum computing. This concept denotes the moment when a quantum processor ceases to manage the noise in its system, resulting in unreliable computations. Recognizing this phase transition is essential for anyone aiming to conduct useful calculations on contemporary quantum processors. As Boixo expressed, “Defining the phase allows for the possibility of discovering applications within that phase on noisy quantum computers, where they will outshine classical computers.”
In simpler terms, by comprehending the boundaries of noise endurance, researchers can pinpoint particular types of problems where quantum computers can still excel over classical counterparts, despite the presence of noise. This could facilitate the creation of novel quantum algorithms and applications specifically designed to align with the capabilities of current noisy quantum hardware.
### Google’s Emphasis on Error Rates Instead of Qubit Numbers
Implicit in Boixo’s assertions is a rationale behind Google’s methodology in quantum computing advancement. While numerous competitors have been in a race to augment the number of qubits in their quantum processors, Google has opted for a more prudent strategy, concentrating on enhancing the error rates of its existing qubits. As Boixo noted, if a quantum processor cannot engage all of its qubits in a low-noise situation, merely increasing the number of qubits will not elevate its performance.
This concentration on error rates is especially crucial for Google’s Sycamore processor, which lies at the forefront of the company’s quantum computing pursuits. Sycamore gained attention in 2019 when it claimed “quantum supremacy,” executing a calculation that would take a classical supercomputer thousands of years to accomplish. Yet, as Boixo pointed out, the foundational error rate of Sycamore remains a constraining factor, and lowering this error rate is vital for unleashing the processor’s full capabilities.
### The Journey Towards Error-Corrected Logical Qubits
While enhancing the error rates of present qubits is a fundamental step, it is not the final objective. The overarching goal is to generate error-corrected logical qubits, which can carry out reliable computations even amidst noise. However, achieving these error-corrected qubits will demand a substantial increase in qubit counts, considering that each logical qubit consists of numerous physical qubits collaborating to rectify errors.
Google has already initiated trials with error-corrected logical qubits on Sycamore, but these efforts have faced challenges due to the processor’s baseline error rate. This underscores the necessity of perpetually improving the quality of individual qubits while researchers strive to enhance quantum processors to facilitate error correction.
### Conclusion: The Journey Forward for Quantum Computing
In conclusion, Boixo’s observations present a coherent framework for the
