Updates on Quantum Error Correction Roadmaps | NextBigFuture.com

Panel discussion:
Yuval Boger, QuEra Computing (Chief Commercial Officer)
Michael Newman, google Quantum AI (Research Scientist)
Jeremy Stevens, Alice and Bob (Tech Dev Lead)
Pshemek Bienias AWS research Scientist
Jin-Sung Kim, Nvidia.

Nvidia and Infleqtion have announced new breakthrough. Infleqtion, a world leader in neutral atom quantum computing, used the NVIDIA CUDA-Q platform to first simulate, and then orchestrate the first-ever demonstration of a material science experiment on logical qubits, on their Sqale physical quantum processing unit (QPU).

Qubits, the basic units of information in quantum computing, are prone to errors, and far too unreliable to make meaningful predictions. Logical qubits, collections of many noisy physical qubits that encode quantum information such that errors can be corrected, overcome this limitation. Logical qubits can perform quantum computations that are tolerant to environmental noise and hardware faults, also known as fault tolerant quantum computing.

A key test for logical qubits is observing a reduced error rate compared to their constituent, noisy, physical qubits. Infleqtion’s results demonstrate this convincingly across a spectrum of inputs.

QuEra can speed up quantum computer solving by 30 times with new error correction work.

Alice & Bob Published a Quantum Computing Roadmap to 100 Logical Qubits in 2030.

The roadmap details five key milestones in Alice & Bob’s plan to deliver a universal, fault-tolerant quantum computer by 2030:

Milestone 1: Master the Cat Qubit
Achieved in 2024 with the Boson chip series, this milestone established a reliable, reproducible cat qubit capable of storing quantum information while resisting bit-flip errors.
Milestone 2: Build a Logical Qubit
Currently under development with the Helium chip series, this stage focuses on creating the company’s first error-corrected logical qubit operating below the error-correction threshold.
Milestone 3: Fault-Tolerant Quantum Computing
With the upcoming Lithium chip series, Alice & Bob aims to scale multi-logical-qubit systems and demonstrate the first error-corrected logical gate.
Milestone 4: Universal Quantum Computing
The Beryllium chip series will enable a universal set of logical gates enabled by magic state factories and live error correction, unlocking the ability to run any quantum algorithm.
Milestone 5: Useful Quantum Computing
The Graphene chip series, featuring 100 high-fidelity logical qubits, will deliver a quantum computer capable of demonstrating quantum advantage in early industrial use cases by 2030, integrating into existing high-performance computing (HPC) facilities.

Achieving practical quantum advantage requires overcoming the errors inherent in quantum systems. Quantum error correction typically relies on additional qubits to detect and correct these errors, but the resource requirements grow quadratically with complexity, making large-scale, useful quantum computing a significant challenge.

Alice & Bob’s cat qubits offer a promising solution to this bottleneck. These superconducting chips feature an active stabilization mechanism that effectively shields the qubits from some external errors. This unique approach has enabled cat qubits to set the world record for bit-flip protection, one of the two major types of errors in quantum computing, effectively eliminating them.

This protection reduces error correction from a 2D problem to a simpler, 1D problem, enabling error correction to scale more efficiently. As a result, Alice & Bob can produce high-quality logical qubits with 99.9999% fidelity, what they call a “6-nines” logical qubit, using a fraction of the resources required by other approaches.

Quantum Error Correction Decoding with GPU Supercomputers at Nvidia and Quantum Computers

NVIDIA is leveraging its high-performance computing (HPC) capabilities to accelerate quantum computing research and development, particularly in the area of quantum error correction (QEC) decoding. The company’s approach combines classical GPU-based supercomputing with quantum processing units (QPUs) to address the challenges of quantum noise and error correction.

NVIDIA’s Quantum-Classical Computing Integration

NVIDIA has developed several key technologies to integrate high-performance computing with quantum systems:

CUDA-Q Platform

The CUDA-Q platform is an open-source, QPU-agnostic quantum-classical accelerated supercomputing platform. It enables tight integration between quantum computers and supercomputers, allowing researchers to:

– Develop quantum applications for chemical simulations and optimization problems
– Investigate quantum applications in AI, energy, and biology
– Explore quantum computing in fields such as chemistry and material science

DGX Quantum System

NVIDIA’s DGX Quantum system is a GPU-accelerated quantum computing platform that combines:

– The NVIDIA Grace Hopper Superchip
– The CUDA Quantum open-source programming model
– Quantum Machines’ OPX quantum control platform

This system enables sub-microsecond latency between GPUs and QPUs, allowing researchers to build powerful applications that integrate quantum and classical computing.

Quantum Error Correction and Decoding>

One of the primary applications of NVIDIA’s high-performance computing in quantum systems is quantum error correction and decoding. The company is addressing this challenge through several approaches:

### AI-Assisted Decoding

NVIDIA is leveraging artificial intelligence to improve quantum error correction:

– Using GPT models to synthesize quantum circuits
– Employing transformers to decode QEC codes

These AI-driven techniques can potentially speed up the decoding process and improve the accuracy of error correction in quantum systems.

GPU-Accelerated Simulations

NVIDIA’s GPU technology is being used to perform large-scale simulations of quantum devices:

– The company can simulate devices containing up to 40 qubits using H100 GPUs
– These simulations allow researchers to study noise implications in increasingly larger quantum chip designs

By using GPU-accelerated simulations, NVIDIA enables quantum hardware engineers to rapidly scale their system designs and improve error correction strategies.

Hybrid Quantum-Classical Algorithms

NVIDIA’s CUDA-Q platform facilitates the development of hybrid quantum-classical algorithms that can address error correction and decoding:

– Researchers can combine the strengths of classical GPUs and QPUs in a single program
– This approach allows for the development of more sophisticated error correction techniques that leverage both quantum and classical resources

By integrating high-performance computing with quantum systems, NVIDIA is accelerating the development of practical quantum error correction and decoding methods. This work is crucial for advancing quantum computing towards fault-tolerant, large-scale applications in the future.

Nvidia Quantum processor design with simulation of devices.