# The Current State of Quantum Computing

Published 02/23/2024

Quantum computing has matured from being a relatively narrow niche discipline to a tool with the potential to play a prominent role in developing applications to solve complex problems. Here’s a summary of the essential elements of quantum computing, as presented by Gregory T. Byrd of North Carolina State University and Yongshan Ding of Yale University — as well as the innovations that are powering its current ascent.

## The Basic Concepts of Quantum Computing

The basic elements driving modern quantum computing consist of components that often have parallels in traditional computing yet have been suited for the quantum mechanical realm.

### Qubits

A qubit is similar to a bit in traditional computing in that it’s the fundamental unit of a quantum computing system. However, it enables far more complex computations because a single qubit can consist of a superposition of logical states. For example, the quibit |ψ〉= α |0〉 + β|1〉where α and β are complex numbers, and | α |2 + | β |2 = 1.

Therefore, a single qubit is capable of performing multiple calculations simultaneously.

### Gates and Algorithms

Quantum gates manipulate coefficients of basis states. In this way, they perform the same general function as logic gates in traditional computing systems.

Quantum algorithms often follow a pattern in that they:

1. Create a quantum state that encodes a data set or initial condition.
2. Perform operations on the quantum state in order to amplify the answer to a problem while minimizing the states that aren’t of interest.
3. Measure the quantum system to determine which states provide the most useful information.

Thus, quantum algorithms are a lot like traditional computing algorithms, at least when it comes to their logic.

### Quantum Annealing

A quantum annealer is a computational model used to figure out the best solution when faced with a set of potential solutions. A company called D-Wave produces quantum annealers that users can access for business purposes.

### Physical Implementations

Quantum computers are physical systems that behave like qubits. However, because quantum states are impacted by their environment, the quantum computing model only provides an approximation of what happens at the quantum level.

This is why John Preskill introduced the term noisy, intermediate-scale quantum (NISQ), which describes modern quantum computing systems. To reduce the “noisy” factor, there needs to be a scaling of qubits into the millions.

### Superconducting Qubits

A superconducting qubit refers to an electronic circuit where energy levels assume quantum values. To develop superconducting qubits, researchers use 2D films made of superconducting material. It’s then cooled to cryogenic temperatures to avoid thermal energy from disturbing the qubit’s state. Electromagnetic coupling with microwave pulses is used to control the qubit.

### Hardware Technology

The hardware technology needed for quantum computing is still in flux. Trapped ions and superconducting qubits are the most widely known, but there are other approaches on the horizon, such as neutral atoms, photonics, and silicon qubits.

## Systems and Software

At this point, the systems and software needed for quantum computing are limited, but research is underway to develop widely usable solutions. Meeting this challenge involves:

• Building a quantum stack. One first has to build a quantum stack that can work with quantum computing principles, which, as mentioned above, are different from those that drive traditional systems.
• Overcome challenges. This includes the limited availability of qubits and combating long-range entanglement issues.
• Co-design hardware and software solutions. Co-designing software and hardware solutions ensures that applications take into account the limitations of the hardware they will be running on.
• Develop fault-tolerant quantum architecture. This involves accounting for the natural variations in a quantum system’s environment by isolating qubits from environmental factors while still enabling them to interact with each other.
• Integrating classical and quantum computing. For instance, classical pre- or post-processing is needed to make algorithms useful — including those developed through quantum computing.

## Top Five Challenges and Opportunities in Quantum Computing

Despite the progress made in quantum computing, as mentioned above, there are some challenges that need to be overcome before quantum computing’s potential can be fully realized. There are also some opportunities that the computing, scientific, and business communities can take advantage of.

1. The availability of qubits. There currently aren’t enough high-quality, error-corrected qubits. To overcome this, it’s important to leverage quantum memory management, which can involve matching qubits with tasks they’re well suited for.
2. Limited connectivity makes long-range entanglement infeasible. Ideally, we could use entanglement to manipulate many qubits to perform a single operation. But currently, a lot of devices with limited connectivity are being built, which makes long-distance entanglement difficult.
3. Limited support for circuit-level fault tolerance. While there has been progress in error correction for creating fault-tolerant qubits, it’s still a challenge to integrate them into universal computing systems.
4. Verification and debugging. Due to the effect of measurement at the quantum level, it’s very difficult to verify and debug quantum computation, especially when it comes to larger systems.
5. Quantum computing as-a-service. Cloud-based quantum computing services, which involve deploying quantum computers in the cloud, are making quantum computing much more accessible to the masses.

## Community and Education

To fully introduce quantum computing on a large scale, engineers need to learn how to design systems for commercial consumption. This requires educational shifts at the high school and university level, as well as current engineers being willing to pivot to designing quantum computing solutions. There are some online resources, such as IBM’s Qiskit textbook and the IEEE Quantum Initiative, which can make learning about quantum computing easier.

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