Dynex

Dynex

Dynex

# Quantum vs n.quantum Computing

### Quantum vs n.quantum Computing

#### Quantum vs n.quantum Computing

### Quantum Computing

Quantum computing is an emerging technology with enormous potential to solve complex problems, because it effectively applies the properties of quantum mechanics, such as superposition and entanglement. However, like any technology, there are disadvantages in some of the architectures:

**Error Correction**

Just like in classical computing’s early days, error correction is a major painpoint for quantum computing today. Quantum computers are sensitive to noise and difficult to calibrate. Unlike traditional computers that would experience a bit flip from 0 to 1 or vice versa, quantum errors are more difficult to correct because qubits can take an infinite number of states.

**Hardware & Temperature**

Because quantum computers need to slow down atoms to near stillness, their processors must be kept at or around absolute zero (-273°C). Even the tiniest of fluctuations can cause unwanted movement, so it’s just as important to make sure that they’re under no atmospheric pressure and that they are insulated from the earth’s magnetic field.

**Scalability**

While quantum computers have shown impressive performance for some tasks, they are still relatively small compared to classical computers. Scaling up quantum computers to hundreds or thousands of qubits while maintaining high levels of coherence and low error rates remains a major challenge.

#### n.quantum Computing

**Neuromorphic Quantum Computing** (abbreviated as ‘**n.quantum computing**’) is an unconventional computing type of computing that uses neuromorphic computing to perform quantum operations. It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with neuromorphic quantum computing.

Neuromorphic quantum computers do not face these limitations. Operated on traditional hardware (Graphic Processing Units, GPUs) at room temperature, there is almost no limitation in number of bits being calculated. Problems with millions of variables and constraints can be computed efficiently on a n.quantum computing machine.

In nature, physical systems tend to evolve toward their lowest energy state: objects slide down hills, hot things cool down, and so on. This behaviour also applies to neuromorphic systems. To imagine this, think of a traveler looking for the best solution by finding the lowest valley in the energy landscape that represents the problem. Classical algorithms seek the lowest valley by placing the traveler at some point in the landscape and allowing that traveler to move based on local variations. While it is generally most efficient to move downhill and avoid climbing hills that are too high, such classical algorithms are prone to leading the traveler into nearby valleys that may not be the global minimum. Numerous trials are typically required, with many travellers beginning their journeys from different points.

In contrast, neuromorphic annealing begins with the traveler simultaneously occupying many coordinates thanks to the phenomenon of inherent parallelism. The probability of being at any given coordinate smoothly evolves as annealing progresses, with the probability increasing around the coordinates of deep valleys. Instantonic jumps allows the traveller to pass through hills—rather than be forced to climb them—reducing the chance of becoming trapped in valleys that are not the global minimum. Long range order further improves the outcome by allowing the traveler to discover correlations between the coordinates that lead to deep valleys. To speed computation, our neuromorphic platform taps directly into an unimaginably vast fabric of reality—the strange and counterintuitive world of physics and biology inspired computing. Rather than store information using bits represented by 0s or 1s as conventional computers do, neuromorphic computers use voltages and current. The dynamic long range behaviour, along with its trend towards optimal energy and instantonic effects, enable neuromorphic computers to consider and manipulate many combinations of bits simultaneously.

#### Learn more

### Quantum Computing

Quantum computing is an emerging technology with enormous potential to solve complex problems, because it effectively applies the properties of quantum mechanics, such as superposition and entanglement. However, like any technology, there are disadvantages in some of the architectures:

**Error Correction**

Just like in classical computing’s early days, error correction is a major painpoint for quantum computing today. Quantum computers are sensitive to noise and difficult to calibrate. Unlike traditional computers that would experience a bit flip from 0 to 1 or vice versa, quantum errors are more difficult to correct because qubits can take an infinite number of states.

**Hardware & Temperature**

Because quantum computers need to slow down atoms to near stillness, their processors must be kept at or around absolute zero (-273°C). Even the tiniest of fluctuations can cause unwanted movement, so it’s just as important to make sure that they’re under no atmospheric pressure and that they are insulated from the earth’s magnetic field.

**Scalability**

While quantum computers have shown impressive performance for some tasks, they are still relatively small compared to classical computers. Scaling up quantum computers to hundreds or thousands of qubits while maintaining high levels of coherence and low error rates remains a major challenge.

#### n.quantum Computing

**Neuromorphic Quantum Computing** (abbreviated as ‘**n.quantum computing**’) is an unconventional computing type of computing that uses neuromorphic computing to perform quantum operations. It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with neuromorphic quantum computing.

Neuromorphic quantum computers do not face these limitations. Operated on traditional hardware (Graphic Processing Units, GPUs) at room temperature, there is almost no limitation in number of bits being calculated. Problems with millions of variables and constraints can be computed efficiently on a n.quantum computing machine.

In nature, physical systems tend to evolve toward their lowest energy state: objects slide down hills, hot things cool down, and so on. This behaviour also applies to neuromorphic systems. To imagine this, think of a traveler looking for the best solution by finding the lowest valley in the energy landscape that represents the problem. Classical algorithms seek the lowest valley by placing the traveler at some point in the landscape and allowing that traveler to move based on local variations. While it is generally most efficient to move downhill and avoid climbing hills that are too high, such classical algorithms are prone to leading the traveler into nearby valleys that may not be the global minimum. Numerous trials are typically required, with many travellers beginning their journeys from different points.

In contrast, neuromorphic annealing begins with the traveler simultaneously occupying many coordinates thanks to the phenomenon of inherent parallelism. The probability of being at any given coordinate smoothly evolves as annealing progresses, with the probability increasing around the coordinates of deep valleys. Instantonic jumps allows the traveller to pass through hills—rather than be forced to climb them—reducing the chance of becoming trapped in valleys that are not the global minimum. Long range order further improves the outcome by allowing the traveler to discover correlations between the coordinates that lead to deep valleys. To speed computation, our neuromorphic platform taps directly into an unimaginably vast fabric of reality—the strange and counterintuitive world of physics and biology inspired computing. Rather than store information using bits represented by 0s or 1s as conventional computers do, neuromorphic computers use voltages and current. The dynamic long range behaviour, along with its trend towards optimal energy and instantonic effects, enable neuromorphic computers to consider and manipulate many combinations of bits simultaneously.

#### Learn more

### Quantum Computing

Quantum computing is an emerging technology with enormous potential to solve complex problems, because it effectively applies the properties of quantum mechanics, such as superposition and entanglement. However, like any technology, there are disadvantages in some of the architectures:

**Error Correction**

Just like in classical computing’s early days, error correction is a major painpoint for quantum computing today. Quantum computers are sensitive to noise and difficult to calibrate. Unlike traditional computers that would experience a bit flip from 0 to 1 or vice versa, quantum errors are more difficult to correct because qubits can take an infinite number of states.

**Hardware & Temperature**

Because quantum computers need to slow down atoms to near stillness, their processors must be kept at or around absolute zero (-273°C). Even the tiniest of fluctuations can cause unwanted movement, so it’s just as important to make sure that they’re under no atmospheric pressure and that they are insulated from the earth’s magnetic field.

**Scalability**

While quantum computers have shown impressive performance for some tasks, they are still relatively small compared to classical computers. Scaling up quantum computers to hundreds or thousands of qubits while maintaining high levels of coherence and low error rates remains a major challenge.

#### n.quantum Computing

**Neuromorphic Quantum Computing** (abbreviated as ‘**n.quantum computing**’) is an unconventional computing type of computing that uses neuromorphic computing to perform quantum operations. It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with neuromorphic quantum computing.

Neuromorphic quantum computers do not face these limitations. Operated on traditional hardware (Graphic Processing Units, GPUs) at room temperature, there is almost no limitation in number of bits being calculated. Problems with millions of variables and constraints can be computed efficiently on a n.quantum computing machine.

In nature, physical systems tend to evolve toward their lowest energy state: objects slide down hills, hot things cool down, and so on. This behaviour also applies to neuromorphic systems. To imagine this, think of a traveler looking for the best solution by finding the lowest valley in the energy landscape that represents the problem. Classical algorithms seek the lowest valley by placing the traveler at some point in the landscape and allowing that traveler to move based on local variations. While it is generally most efficient to move downhill and avoid climbing hills that are too high, such classical algorithms are prone to leading the traveler into nearby valleys that may not be the global minimum. Numerous trials are typically required, with many travellers beginning their journeys from different points.

In contrast, neuromorphic annealing begins with the traveler simultaneously occupying many coordinates thanks to the phenomenon of inherent parallelism. The probability of being at any given coordinate smoothly evolves as annealing progresses, with the probability increasing around the coordinates of deep valleys. Instantonic jumps allows the traveller to pass through hills—rather than be forced to climb them—reducing the chance of becoming trapped in valleys that are not the global minimum. Long range order further improves the outcome by allowing the traveler to discover correlations between the coordinates that lead to deep valleys. To speed computation, our neuromorphic platform taps directly into an unimaginably vast fabric of reality—the strange and counterintuitive world of physics and biology inspired computing. Rather than store information using bits represented by 0s or 1s as conventional computers do, neuromorphic computers use voltages and current. The dynamic long range behaviour, along with its trend towards optimal energy and instantonic effects, enable neuromorphic computers to consider and manipulate many combinations of bits simultaneously.